model-conversion : add embedding prompt file support (#15871)

This commit adds support for passing a prompt file to the model
conversion targets/scripts. It also updates the logits.cpp to print out
embedding information in the same format as when running the original
embedding model.

The motivation for this is that it allows us to pass files of different
sizes when running the converted models and validating the logits.

This can be particularly important when testing the sliding window
functionality of models where the sequence length needs to exceed a
certain number of tokens to trigger the sliding window logic.

examples/model-conversion/scripts/embedding/run-original-model.py

@@ -13,14 +13,37 @@ unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')
 
 parser = argparse.ArgumentParser(description='Process model with specified path')
 parser.add_argument('--model-path', '-m', help='Path to the model')
+parser.add_argument('--prompts-file', '-p', help='Path to file containing prompts (one per line)')
 args = parser.parse_args()
 
+def read_prompt_from_file(file_path):
+    try:
+        with open(file_path, 'r', encoding='utf-8') as f:
+            return f.read().strip()
+    except FileNotFoundError:
+        print(f"Error: Prompts file '{file_path}' not found")
+        exit(1)
+    except Exception as e:
+        print(f"Error reading prompts file: {e}")
+        exit(1)
+
 model_path = os.environ.get('EMBEDDING_MODEL_PATH', args.model_path)
 if model_path is None:
     parser.error("Model path must be specified either via --model-path argument or EMBEDDING_MODEL_PATH environment variable")
 
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 
+config = AutoConfig.from_pretrained(model_path)
+
+# This can be used to override the sliding window size for manual testing. This
+# can be useful to verify the sliding window attention mask in the original model
+# and compare it with the converted .gguf model.
+if hasattr(config, 'sliding_window'):
+    original_sliding_window = config.sliding_window
+    #original_sliding_window = 6
+    print(f"Modified sliding window: {original_sliding_window} -> {config.sliding_window}")
+
+print(f"Using unreleased model: {unreleased_model_name}")
 if unreleased_model_name:
     model_name_lower = unreleased_model_name.lower()
     unreleased_module_path = f"transformers.models.{model_name_lower}.modular_{model_name_lower}"
@@ -29,19 +52,28 @@ if unreleased_model_name:
 
     try:
         model_class = getattr(importlib.import_module(unreleased_module_path), class_name)
-        model = model_class.from_pretrained(model_path)  # Note: from_pretrained, not fromPretrained
+        model = model_class.from_pretrained(model_path, config=config)
     except (ImportError, AttributeError) as e:
         print(f"Failed to import or load model: {e}")
         exit(1)
 else:
-    model = AutoModel.from_pretrained(model_path)
+    model = AutoModel.from_pretrained(model_path, config=config)
 print(f"Model class: {type(model)}")
-#print(f"Model file: {type(model).__module__}")
-config = AutoConfig.from_pretrained(model_path)
+print(f"Model file: {type(model).__module__}")
+
+# Verify the model is using the correct sliding window
+if hasattr(model.config, 'sliding_window'):
+    print(f"Model's sliding_window: {model.config.sliding_window}")
+else:
+    print("Model config does not have sliding_window attribute")
 
 model_name = os.path.basename(model_path)
 
-texts = [ "Hello world today" ]
+if args.prompts_file:
+    prompt_text = read_prompt_from_file(args.prompts_file)
+    texts = [prompt_text]
+else:
+    texts = ["Hello world today"]
 
 encoded = tokenizer(
     texts,